DATA SCIENTIST

Job purpose

The Data Scientist is responsible for designing, developing, validating, deploying, and continuously improving data science and machine learning solutions that deliver measurable business value in banking, while complying with the Bank’s AI governance, model risk management, data governance, information security, privacy, and regulatory requirements. The role ensures models and analytical solutions are accurate, explainable, fair, secure, well-documented, and fit for purpose throughout their lifecycle.
Principal responsibilities
Design, develop, and implement predictive, prescriptive, and optimization models for priority banking use cases such as fraud detection, credit risk assessment, collections, customer analytics, and operational efficiency.
Translate business problems into data science use cases, define success criteria with stakeholders, and ensure proposed solutions align with approved business objectives and governance requirements.
Perform data exploration, feature engineering, model training, testing, and performance evaluation using sound statistical and machine learning techniques.
•

Prepare complete model documentation, including business rationale, methodology, assumptions, data sources, feature definitions, limitations, performance metrics, and implementation considerations, to support review, approval, audit, and regulatory scrutiny.
•

Ensure models are developed and maintained in line with the Bank’s AI governance framework, model risk management standards, data governance requirements, responsible AI principles, and applicable regulatory obligations.
•

Support model validation and approval processes by providing transparent documentation, reproducible development artefacts, evidence of testing, and clear explanations of model logic, outputs, and limitations.
•

Assess and mitigate risks relating to model bias, unfair outcomes, data quality, privacy, explainability, robustness, and misuse, and escalate material issues through the appropriate governance channels.
•

Collaborate with Data Engineering, MLOps, IT, Risk, Compliance, Information Security, Internal Audit, and business teams to ensure controlled deployment, integration, monitoring, and change management for analytical solutions.
•

Monitor models and analytical solutions in production for performance, stability, drift, fairness, and operational effectiveness, and recommend recalibration, retraining, rollback, or retirement where required.
•